Overview

Today, we will return to exploring Russians’ support the war in Ukraine using a public opinion survey from Russia conducted by Alexei Miniailo’s “Do Russians Want War” project.

The survey was conducted by phone using a random sample of mobile phone numbers to produce a sample of respondents representative of the population in terms of age, sex, and geography. It was in the field from February 28 to March 2.

First, we will explore how support for the war varies with the demographic predictors age, sex and education. We will compare the results of modeling this relationship using Ordinary Least Squares regression and Logisitic Regression

We’ll talk more about the technical aspects of logistic regression next week. For today we’ll simply compare the results from these two approaches.

Next, we will gain insight into how our estimates from these models might vary using the statistical process of bootstrapping. Specifically, we will simulate the idea of repeated sampling that is the foundation of frequentist interpretations of probability, by sampling from our sample with replacement.

We’ll walk through the mechanics of simulation together. Then you’ll quantify the uncertainty described by these bootstrapped sampling distributions.

Finally, we’ll see what other questions we might ask of these data and practice various skills we’ve developed through out the course.

Plan to spend the following amount of time on each section

Get set up to work (5 minutes)
Model the relationship between demographic predictors and war support using OLS and Logistic regression (20 minutes)
Assess the uncertainty around your estimated coefficients (15 minutes)
Quantify the uncertainy described by your sampling distributions (10 minutes)
Explore other relationships in the data. (30 minutes)

The graded question for today is:

set.seed(472022)
graded_question <- sample(1:5,size = 1)
paste("Question",graded_question,"is the graded question for this week")

[1] "Question 5 is the graded question for this week"

You will work in your assigned groups. Only one member of each group needs to submit the html file produced by knitting the lab.

This lab must contain the names of the group members in attendance.

If you are attending remotely, you will submit your labs individually.

You can find your assigned groups in previous labs

Goals

Today’s lab covers a little bit of everything. You will

learn how to model binary outcomes using logistic regression
develop an intuition for how to think about uncertainty using simulations to describe what might have happened
practice formulating, estimating, presenting and interpreting regression models.

Workflow

Please knit this .Rmd file

As with every lab, you should:

Download the file
Save it in your course folder
Update the author: section of the YAML header to include the names of your group members in attendance.
Knit the document
Open the html file in your browser (Easier to read)
Write yourcode in the chunks provided
Comment out or delete any test code you do not need
Knit the document again after completing a section or chunk (Error checking)
Upload the final lab to Canvas.

1 Get setup to work

1.1 Load Packages

First lets load the libraries we’ll need for today.

the_packages <- c(
  ## R Markdown
  "kableExtra","DT","texreg","htmltools",
  "flair", # Comments only
  ## Tidyverse
  "tidyverse", "lubridate", "forcats", "haven", "labelled",
  "modelr", "purrr",
  ## Extensions for ggplot
  "ggmap","ggrepel", "ggridges", "ggthemes", "ggpubr", 
  "GGally", "scales", "dagitty", "ggdag", "ggforce",
  # Data 
  "COVID19","maps","mapdata","qss","tidycensus", "dataverse",
  # Analysis
  "DeclareDesign", "boot"
)

# Define function to load packages
ipak <- function(pkg){
    new.pkg <- pkg[!(pkg %in% installed.packages()[, "Package"])]
    if (length(new.pkg)) 
        install.packages(new.pkg, dependencies = TRUE)
    sapply(pkg, require, character.only = TRUE)
}

ipak(the_packages)

   kableExtra            DT        texreg     htmltools         flair 
         TRUE          TRUE          TRUE          TRUE          TRUE 
    tidyverse     lubridate       forcats         haven      labelled 
         TRUE          TRUE          TRUE          TRUE          TRUE 
       modelr         purrr         ggmap       ggrepel      ggridges 
         TRUE          TRUE          TRUE          TRUE          TRUE 
     ggthemes        ggpubr        GGally        scales       dagitty 
         TRUE          TRUE          TRUE          TRUE          TRUE 
        ggdag       ggforce       COVID19          maps       mapdata 
         TRUE          TRUE          TRUE          TRUE          TRUE 
          qss    tidycensus     dataverse DeclareDesign          boot 
         TRUE          TRUE          TRUE          TRUE          TRUE

There are three packages in particular that we’ll need to maker sure are installed and loaded

modelr
purrr
broom

If ipak didn’t return TRUE for each of these, please uncomment and run:

# install.packages("modelr")
# install.packages("purrr")
# install.packages("broom")

1.2 Load the data

Next we’ll load the recoded data for the lab

Our primary outcome of interest (dependent variable) for today is a binary measure of support for war:

support_war01 “Please tell me, do you support or do not support Russia’s military actions on the territory of Ukraine?” (1=yes, 0 = no)

Our key predictors (independent variables) are the following demographic variables:

age “How old are you?”
sex “Gender of respondent” (As assessed by the interviewer)
education_n “What is your highest level of education (confirmed by a diploma, certificate)?” (1 = Primary school, 2 = “High School”, 3 = “Vocational School” 4 = “College”, 5 = Graduate Degree)¹

load(url("https://pols1600.paultesta.org/files/data/df_drww.rda"))

df_drww %>%
  mutate(
    person_id = 1:n()
  ) -> df_drww

2 Model the relationship between demographic predictors and war support using OLS and Logistic regression.

2.1 Fit the models

Please estimate the following models:

An OLS regression model called m1 using lm()
A Logistic regression model called m2 using glm() with family=binomial

# OLS
m1 <- lm(support_war01 ~ age + sex + education_n, df_drww)

# Logisitic 
m2 <- glm(support_war01 ~ age + sex + education_n, df_drww,
          family = binomial)

2.2 Display the results in a regression table

Next, please display the results of your regressions in a table using htmlreg()

# Regression Table
htmlreg(list(m1,m2))

Statistical models
	Model 1	Model 2
(Intercept)	0.28^***	-1.36^***
	(0.05)	(0.29)
age	0.01^***	0.05^***
	(0.00)	(0.00)
sexMale	0.09^***	0.50^***
	(0.02)	(0.13)
education_n	-0.02	-0.10
	(0.01)	(0.06)
R²	0.11
Adj. R²	0.11
Num. obs.	1463	1463
AIC		1575.54
BIC		1596.69
Log Likelihood		-783.77
Deviance		1567.54
^*p < 0.001; ^p < 0.01; ^*p < 0.05

2.3 Produce predicted values from the model

The coefficients from a logistic regression aren’t easy to directly interpret.

Instead, we will produce predicted values for each model

To do this, we will need to create a prediction dataframe called pred_df Every variable in your model, needs to be represented in your prediction data frame.

Use expand_grid() to create a data frame where
- age varies from 18 to 99
- sex is held constant at “Female”
- education_n is held constant at it’s mean

## Create prediction data frame
pred_df <- expand_grid(
  age = 18 : 99,
  sex = "Female",
  education_n = mean(df_drww$education_n, na.rm = T)
)

Then you use the predict() function to produce predicted values from each model.

Save the output of predict() for m1 to a new column in pred_df called pred_ols.

For m2 you need to tell are to transform the predictions from m2 back into the units of the response (outcome) variable, by setting the argument type = "response". Save the output of predict() for m1 to a new column in pred_df called pred_logit.

# #Predicted values for m1
pred_df$pred_ols <- predict(m1,
                            newdata = pred_df)
# Predicted values for m2
# Remember to add type = "response"
pred_df$pred_logit <- predict(m2,
                            newdata = pred_df,
                            type = "response")

2.4 Plot the predicted values and interpet the results

Now we can compare the predictions of OLS and Logistic regression by plotting the predicted values of support for the war from each model.

To produce this plot you’ll need to

specify data (you want to use the values from pred_df)
map values from your data aesthetics in ggplot
- put age on the x axis and and pred_ols on the y-axis
specify the geometries to plot
- add two geom_line() to the plot
- leave the first one empty (e.g. geom_line())
- for the second, specify a new aes of y=pred_logit

# data
pred_df%>%
  # aesthetics
  ggplot(aes(age, pred_ols, col = "OLS"))+
  # geometries
  geom_line()+
  geom_line(aes(y = pred_logit, col = "Logistic"))+
  geom_jitter(data=df_drww, aes(age, support_war01),
              col = "black",
              height = .05,
              size = .5,
              alpha = .5)+
  labs(
    col = "Model",
    x = "Age",
    y = "Predicted Values"
  )

Warning: Removed 334 rows containing missing values (geom_point).

How do the predictions of the two models compare

So the predictions from OLS produce impossible values (levels of support above 100 percent) at for very old respondents, while the predictions from logistic regression are constrained to be between 0 and 1.

If we think that logistic regression provides a more credible way of modeling support for the war, then our OLS regression appears to overstate the level of support among young and old, while possibly understating the level of support among the middle age. The differences aren’t huge – a few percentage points – but for a binary outcome we will often prefer to model it with logistic regression.

Also note that marginal effect for age in the logistic regression is not constant. An increase in age from 25 to 26 is associated with a larger increase in support, than an increase in age from 75 to 76.

3 Assess the uncertainty around your estimated coefficients

How confident are we that the true relationship between age and support for the war is positive? How different might the coefficient be if we had taken a different random sample of Russians in early March 2022?

In this section, we will explore how we can simulate the idea “repeated” sampling using just the sample we have to quantify uncertainty about the estimates in our model.

To do this we will.

Take 1,000 bootstrap samples from df_drww with replacement using the bootstrap function from the modelr package.

By sampling with replacement, we allow the same observation to be included multiple times (or not all), in our bootstrapped sample.

For each bootstrap sample, we will estimate the same model as above, using the map function from the purrr package.

We will end up with 1,000 bootstrapped estimates for each coefficient in our model
These bootstrapped estimates describe the sampling distribution of coefficient.

Put the coefficients from this bootstrapped model into into a tidy data frame, using the tidy function from the broom package, and the map_df function from the purrr package.
Visualize the sampling distribution for coefficients in our model.

In the code below I demonstrate this process for m1. Then you will repeat this process for m2

3.1 Take 1,000 bootstrap samples from `df_drww`

Below we create 1,000 boostrapped samples

# Make sure these packages are loaded
library(modelr)
library(purrr)
library(broom)
# Set random seed for reproducability

set.seed(1234)

# 1,000 bootstrap samples
boot <- modelr::bootstrap(df_drww, 1000)

Let’s take a moment to understand what boot is and why we’re sampling with replacement.

The object boot contains 1,000 bootstrapped samples from df_drww.

If we look at the first bootstrap we see:

boot$strap[[1]]

<resample [1,807 x 43]> 1308, 1018, 1125, 1004, 623, 905, 645, 934, 400, 900, ...

The numbers 1308, 1018, 1125, 1004, 623, 905, ... correspond to rows in df_drww. So person 1308 is the first observation in this boot strap sample, then person 1018 and so on.

Because we are sampling with replacement observations from df_drww can appear multiple times. In our first bootstrap sample:

666 observations appeared once
342 appeared twice
105 appeared three times
27 appeared four times
5 appeared five times.
2 appeared six times

table(table(boot$strap[[1]]$idx))


  1   2   3   4   5   6 
666 342 104  27   5   2

Why would we want to sample with replacement?

Well, what we’d really love are 1,000 separate random surveys all drawn from the same population in the same way.

Since that’s not feasible, we instead use the one sample we do have to learn things like how much our estimate might vary in repeated sampling. Efron (1979) called this procedure “bootstrapping” after the idiom “to pull oneself up by one’s own bootstraps”

We do this by sampling from our sample with replacement.

When we sample with replacement, we are sampling from our sample, as our sample was sampled from the population.

With replacement means that some observations will appear multiple times in our bootstrapped sample (while others will not be included at all).

When an observation appears multiple times in a bootstrap sample, conceptually, we’re using that original observation to represent the other people like that observation in the population who – had we taken a different sample – might have been included in our data.

Note each bootstrap sample is a different random sample with replacement. In our second bootstrap sample, one observation (person 1496) appeared five times.

table(table(boot$strap[[2]]$idx))


  1   2   3   4   5   6 
661 342 105  31   1   3

# Person 406
sum(boot$strap[[2]]$idx == 1496)

[1] 5

# Person 1 is not in boostrap 2
sum(boot$strap[[2]]$idx == 1)

[1] 0

3.2 Estimate 1,000 models from the 1,000 bootstrapped samples

Now let’s estimate our model for each bootstrapped sample, using the map function.

In the code below, for every sample in boot, map estimates the model lm(support_war01 ~ age + sex + education_n) plugging in the bootstrap sample into the data=..

# bootstrap simulations
bs_ols <- purrr::map(boot$strap, ~ lm(support_war01 ~ age + sex + education_n, data =.))

The end result is a large list with 1,000 separate linear regression models estimated on each bootstrapped sample.

The coefficients from each bootstrap vary from one simulation

# First bootstrap
bs_ols[[1]]


Call:
lm(formula = support_war01 ~ age + sex + education_n, data = .)

Coefficients:
(Intercept)          age      sexMale  education_n  
   0.305019     0.009746     0.081039    -0.024142

to the next:

# Second boostrap
bs_ols[[2]]


Call:
lm(formula = support_war01 ~ age + sex + education_n, data = .)

Coefficients:
(Intercept)          age      sexMale  education_n  
   0.245000     0.009142     0.095631    -0.009285

Because they’re estimated off of different bootstrapped samples.

We will visualize and quantify that variation to describe the uncertainty associated with our estimates.

But first, we need to transform our large list of linear models, into a more tidy data frame that’s easier to manipulate.

3.3 Tidy the results of our bootstrapping

In the code below we transform this large list of models into a tidy data frame of coefficients.

# Tidy bootstrapp sims
bs_ols_df <- map_df(bs_ols, tidy, .id = "id")

In the resulting data frame the id variable identifies the bootstrap simulation (1 to 1,000), the term variable indentifies the specific coefficient from the model estimated for that simulation.

head(bs_ols_df)

# A tibble: 6 × 6
  id    term        estimate std.error statistic  p.value
  <chr> <chr>          <dbl>     <dbl>     <dbl>    <dbl>
1 1     (Intercept)  0.305    0.0522        5.84 6.29e- 9
2 1     age          0.00975  0.000702     13.9  3.31e-41
3 1     sexMale      0.0810   0.0222        3.65 2.73e- 4
4 1     education_n -0.0241   0.0107       -2.26 2.39e- 2
5 2     (Intercept)  0.245    0.0533        4.60 4.61e- 6
6 2     age          0.00914  0.000731     12.5  3.36e-34

3.4 Plot the bootstrapped sampling distribution of the coefficient for age

Finally, let’s get a sense of how our coefficients could vary.

Specifically, let’s compare the the observed coefficient from m1 for age, to the bootstrapped sampling distribution of coefficients in bs_ols_df

First, we’ll create a basic plot called p_ols_age that shows the distribution of the coefficients for age from our simulation

p_ols_age <- bs_ols_df %>%
  filter(term == "age") %>%
  ggplot(aes(estimate))+
    geom_density()

p_ols_age

Next we’ll add some additional geometries and labels to our figure

First we’ll put a rug to show the individual coefficients

p_ols_age +
  geom_rug() -> p_ols_age

p_ols_age

Then we’ll add a vertical line where our observed coefficient on age

p_ols_age +
  geom_vline(xintercept =  coef(m1)[2],
             linetype = 2) -> p_ols_age
p_ols_age

Finally, let’s add some nice labels

p_ols_age +
  theme_bw()+
  labs(
    x = "Age",
    y = "",
    title = "Bootstrapped Sampling Distribution of Age Coefficient"
  ) -> p_ols_age

p_ols_age

Congratulations you’ve just produced and visualized your first bootstrapped sampling distribution!

Conceptually, this distribution describes *how much we would expect the coefficient on age in model to vary** from sample to sample.

Just from eyeballing the figure above, it looks like the observed the relationship between age and support for war or 0.009 could be about as high as 0.011, and as low as 0.007.

Of course, as the budding quantitative social scientists that we are, we can do better than just eyeballing the data.

4 Quantify the uncertainy described by your sampling distributions

Specifically, please calculate the standard deviations, 97.5 and 2.5 percentiles of each coefficient in bs_old_df

You’ll want to

group_by() the term variable in bs_ols_df
summarize() the output of functions applied to the estimate column in bs_ols_df

bs_ols_df %>%
  group_by(term)%>%
  summarize(
    mean_estimate = mean(estimate),
    sd = sd(estimate,na.rm=T),
    p2_5 = quantile(estimate, prob =.025),
    p97_5 = quantile(estimate, prob =.975)
  )

# A tibble: 4 × 5
  term        mean_estimate       sd     p2_5   p97_5
  <chr>               <dbl>    <dbl>    <dbl>   <dbl>
1 (Intercept)       0.278   0.0551    0.168   0.390  
2 age               0.00919 0.000713  0.00780 0.0105 
3 education_n      -0.0156  0.0110   -0.0363  0.00704
4 sexMale           0.0940  0.0232    0.0494  0.140

Please compare these these estimates to those by the following functions

# summary(m1)
# confint(m1)

They’re similar to a few thousandsth of a decimal place.

In fact, we can approximate the sampling distribution of coefficients two ways:

Using simulation based approaches like the bootstrap procedure we implemented above
Using asymptotic theory (i.e the Central Limit Theorem) to derive the theoretical sampling distributions

Below we see compare the two approaches (bootstrapped distribution in black, asymptotic normal distribution in red dots) and see they look quite similar.

p_ols_age +
  stat_function(
    fun = "dnorm", 
    args = list(mean =coef(m1)[2],
                sd = summary(m1)$coef[2,2]),
    col = "red",
    linetype = 3
  )

5 Explore other relationships in the data.

Finally, let’s take some time to explore other variables and relationships in the data.

Please do the following:

Research Question: Formulate a brief research question here
Data: Identify the relevant outcome and predictor variables in the data.
- Outcome:
- Key Predictor:
- Covariates:
  - Covariate 1

# Explore the data

If necessary, transform and recode the data as needed

# Data transformations if necessary

Models and Expecations Specify a model or models to test your question in terms of our outcome and predictor variables.

\[\text{Outcome} = \beta_0 + \beta_1 \text{Key Predictor} + \beta_2 \text{Covariate}\]

Expectations Discuss the expected sign and significance of the coefficients in your model. If you think a coefficient could be either positive or negative, explain what each of these results means in the context of your question.
Estimation Estimate a model or models to test your question.

# Models

Results Present and interpret your results using tables and figures.

# Regression table

# Figures

Research Question: What effect does social media use have on Russian’s Support for the War? Does the effect of social media vary with age
Data:
- Outcome: support_war01
- Key Predictor: total_social_media_use a count from 0 (none) to 9 social media sites
- Additional predictors:
  - Age
  - Sex
  - Education
  - Employment
Data transformations: To simplify interpretation, I will create some indicator variables
- under30 an indicator that takes a value of 1 the respondent is under 30 and 0 otherwise
Models and Expectations
Baseline Model:

\[\text{Support} = \beta_0 + \beta_1 \text{social media} +\epsilon\] - Expectations:

If \(\beta_1\) is positive this would suggest that higher social media use associated with greater support for the war. Perhaps respondents in russia are more likely to encounter state propaganda
If \(\beta_1\) is negative, this implies that greater social media use is associated with less support. Perhaps sites like Twitter/Facebook/TikTok in early March allowed Russians to encounter non-state media
Baseline Model with Controls:

\[\text{Support} = \beta_0 + \beta_1 \text{social media} + X\beta +\epsilon\] - Expectations: - If the coefficient on \(\beta_1\) is no longer signficant, this suggests that the bivariate relationship was spurious, driven by factors \(X\) that predict both support and social media use. For example, it may be that the educated are more likely to use social media and less likely to support the war. Controlling for education then might remove the association between social media use and support.

Interaction Model with Controls:

\[\text{Support} = \beta_0 + \beta_1 \text{Social Media} +\beta_2\text{Under 30} + \beta_3\text{SM}\times\text{U30} + X\beta +\epsilon\] - Expectations: - The key coefficient in this model is \(\beta_3\) which assesses whether the relationship between social media use and support differs for those under 30 compared to those over 30. It’s possible that young people use social media differently than older folks, and so I’d expect this coefficient to be negative.

## ---- Data
df_drww %>%
  mutate(
    under30 = case_when(
      age < 30 ~ 1,
      T ~ 0
    ),
    any_social = case_when(
      total_social_media_use ==0 ~ 0,
      T ~ 1
    )
  ) -> df_drww

m1_ex <- glm(support_war01 ~ total_social_media_use, 
             df_drww, family = binomial)

m2_ex <- glm(support_war01 ~ total_social_media_use + 
               age + sex + education_n +employ_working, 
             df_drww, family = binomial)

m3_ex <- glm(support_war01 ~ total_social_media_use*under30 + 
               sex + education_n + employ_working, 
             df_drww, family = binomial)

htmlreg(list(m1_ex,m2_ex, m3_ex))

Statistical models
	Model 1	Model 2	Model 3
(Intercept)	1.36^***	-1.20^***	1.70^***
	(0.08)	(0.33)	(0.22)
total_social_media_use	-0.19^***	-0.09^**	-0.17^***
	(0.03)	(0.03)	(0.03)
age		0.05^***
		(0.00)
sexMale		0.42^**	0.29^*
		(0.13)	(0.13)
education_n		-0.11	-0.09
		(0.06)	(0.06)
employ_working		0.29^*	-0.15
		(0.14)	(0.13)
under30			-1.51^***
			(0.27)
total_social_media_use:under30			0.10
			(0.08)
AIC	1695.81	1567.87	1634.30
BIC	1706.40	1599.60	1671.32
Log Likelihood	-845.90	-777.94	-810.15
Deviance	1691.81	1555.87	1620.30
Num. obs.	1473	1463	1463
^*p < 0.001; ^p < 0.01; ^*p < 0.05

Overall, social media use appears to be associated with less support for the war in Ukraine among Russians.

As the first figure below shows, the baseline model, predicting support with only social media use appears to show a dramatic decline of nearly 40 percentage points in support.

Controlling for other factors likely to predict support and social media use, the decrease is less dramatic, but still, going from no social media use to a using a lot (nine) sites is associated with a about a 17 percentage point decrease in support for the war.

Contray to my expectations, the effect of social media doesn’t appear to vary by age cohort. Support decreases with social medial use for both those over and under 30 but the rate of decrease is not statistically different.

pred_df_ex1 <- expand_grid(
  total_social_media_use = 0:9,
  age = mean(df_drww$age, na.rm = T),
  sex = "Female",
  education_n = mean(df_drww$education_n, na.rm = T),
  employ_working = 1
)

pred_df_ex1$pred_m1 <- predict(m1_ex, pred_df_ex1, type ="response")
pred_df_ex1$pred_m2 <- predict(m2_ex, pred_df_ex1, type ="response")

pred_df_ex1%>%
  ggplot(aes(total_social_media_use, pred_m1))+
  geom_line(aes(col = "Bivariate"))+
  geom_line(aes(y=pred_m2, col = "Controls"))+
  theme_bw()+
  labs(
    col = "Model",
    x = "Number of Social Media Sites Used",
    y = "Predicted Support for the War"
  )

pred_df_ex2 <- expand_grid(
  total_social_media_use = 0:9,
  under30 = c(0,1),
  sex = "Female",
  education_n = mean(df_drww$education_n, na.rm = T),
  employ_working = 1
)

pred_df_ex2$pred_m3 <- predict(m3_ex, pred_df_ex2, type ="response")

pred_df_ex2%>%
  mutate(
    Age = case_when(
      under30 == 1 ~ "Under 30",
      T ~ "30 and Over"
    )
  ) %>%
  ggplot(aes(total_social_media_use, pred_m3, col = Age))+
  geom_line()+
  theme_bw()+
  labs(
    x = "Number of Social Media Sites Used",
    y = "Predicted Support for the War"
  )

Young folk are considerably more likely to use social media

df_drww %>%
  mutate(
    Age = case_when(
      under30 == 1 ~ "Under 30",
      T ~ "30 and Over"
    )
  ) %>%
  ggplot(aes(total_social_media_use,fill =Age))+
  geom_histogram(aes(y= ..density..))+
  facet_grid(~Age)+
  labs(
    x = "Social Media Sites Used"
  )

If we were to instead compare those over and under 30 by any social media use vs no social media use, we see that the decrease in support is much larger among the young (although not statistically significant…)

m4_ex <- glm(support_war01 ~ any_social*under30 + 
               sex + education_n + employ_working, 
             df_drww, family = binomial)
summary(m4_ex)


Call:
glm(formula = support_war01 ~ any_social * under30 + sex + education_n + 
    employ_working, family = binomial, data = df_drww)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.0122  -1.0362   0.6890   0.7832   1.4641  

Coefficients:
                   Estimate Std. Error z value Pr(>|z|)    
(Intercept)         1.66940    0.23273   7.173 7.33e-13 ***
any_social         -0.33934    0.15593  -2.176   0.0295 *  
under30            -0.68599    0.55992  -1.225   0.2205    
sexMale             0.31095    0.12411   2.505   0.0122 *  
education_n        -0.09748    0.06212  -1.569   0.1166    
employ_working     -0.22633    0.13364  -1.694   0.0904 .  
any_social:under30 -0.68038    0.58728  -1.159   0.2466    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 1735.7  on 1462  degrees of freedom
Residual deviance: 1643.6  on 1456  degrees of freedom
  (344 observations deleted due to missingness)
AIC: 1657.6

Number of Fisher Scoring iterations: 4

pred_df_ex3 <- expand_grid(
  any_social = 0:1,
  under30 = c(0,1),
  sex = "Female",
  education_n = mean(df_drww$education_n, na.rm = T),
  employ_working = 1
)

pred_df_ex3$pred_m4 <- predict(m4_ex, pred_df_ex3, type ="response")

pred_df_ex3%>%
  mutate(
    Age = case_when(
      under30 == 1 ~ "Under 30",
      T ~ "30 and Over"
    ),
    Media = case_when(
      any_social == 1 ~ "Social Media",
      T ~ "No Social Media"
    )
  ) %>%
  ggplot(aes(Media, pred_m4, fill = Age))+
  geom_bar( stat = "identity")+
  facet_grid(~Age)+
  theme_bw()+
  labs(
    x = "Number of Social Media Sites Used",
    y = "Predicted Support for the War"
  )

I think, google translate was a bit unclear. But higher numbers equal more education.↩︎

Comments for Lab 09 - Exploring Russians’ Attitudes About the War in Ukraine with OLS and Logistic Regression

Your Group Members Names Here

Last Updated 2022-04-07

Overview

Goals

Workflow

Please knit this .Rmd file

1 Get setup to work

1.1 Load Packages

1.2 Load the data

2 Model the relationship between demographic predictors and war support using OLS and Logistic regression.

2.1 Fit the models

2.2 Display the results in a regression table

2.3 Produce predicted values from the model

2.4 Plot the predicted values and interpet the results

3 Assess the uncertainty around your estimated coefficients

3.1 Take 1,000 bootstrap samples from `df_drww`

3.2 Estimate 1,000 models from the 1,000 bootstrapped samples

3.3 Tidy the results of our bootstrapping

3.4 Plot the bootstrapped sampling distribution of the coefficient for age

4 Quantify the uncertainy described by your sampling distributions

5 Explore other relationships in the data.

Comments for Lab 09 - Exploring Russians’ Attitudes About the War in Ukraine with OLS and Logistic Regression

Your Group Members Names Here

Last Updated 2022-04-07

Overview

Goals

Workflow

Please knit this .Rmd file

1 Get setup to work

1.1 Load Packages

1.2 Load the data

2 Model the relationship between demographic predictors and war support using OLS and Logistic regression.

2.1 Fit the models

2.2 Display the results in a regression table

2.3 Produce predicted values from the model

2.4 Plot the predicted values and interpet the results

3 Assess the uncertainty around your estimated coefficients

3.1 Take 1,000 bootstrap samples from df_drww

3.2 Estimate 1,000 models from the 1,000 bootstrapped samples

3.3 Tidy the results of our bootstrapping

3.4 Plot the bootstrapped sampling distribution of the coefficient for age

4 Quantify the uncertainy described by your sampling distributions

5 Explore other relationships in the data.

3.1 Take 1,000 bootstrap samples from `df_drww`